count_documents

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already mark it read-only and non-destructive, and the description adds useful behavioral context: it returns no hits and is cheap to run. This goes beyond the annotations without contradicting them, though it does not describe edge cases like null query semantics or exact response shape.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences with no filler. The core behavior is front-loaded, and the secondary sentence provides a practical reason to use the tool without repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple, low-parameter tool backed by a readOnly annotation and an output schema, the description is mostly complete. The only meaningful gap is query parameter semantics, which is not provided by the schema description either.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, yet the description does little to explain the two parameters. 'Matching documents' implies query filtering, but the description does not clarify that query can be a string, object, or null, nor what a null query does or why the index parameter is required.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific operation: counting matching documents. It also explicitly notes that it does not return hits, immediately distinguishing it from search-style sibling tools like paged_search or run_query.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives a concrete use case: 'cheap way to size a query before running it,' which tells an agent when choosing this tool is appropriate. It does not explicitly list exclusions or alternatives, but the guidance is still clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.